The modern world is facing increasing challenges in the field of energy consumption, where a significant share is attributed to the heating of buildings. Inefficient use of energy in heating systems leads to unjustified economic costs and negative environmental impact. According to the International Energy Agency, heating and cooling of buildings accounts for about 30% of global energy consumption [1]. This emphasises the need to move towards more energy efficient and sustainable approaches in heat management. Currently, the integration of intelligent control systems based on the Internet of Things and artificial intelligence technologies is actively developing, which opens new horizons for improving energy efficiency. The use of these technologies allows not only to reduce overall energy consumption, but also to create more comfortable and adaptive conditions for living and working in buildings. In this regard, analysing and comparing heating control methods with the use of intelligent systems is an important step in the development of sustainable and energy efficient urban environments.

Traditional heating control systems, which are widely used today, are characterised by low efficiency and significant energy losses. These systems usually include manual control, simple thermostats and time programmes. Manual control involves the user setting the parameters of the heating system, which results in inefficient energy consumption due to human influence and lack of adaptation to changes in external conditions. Simple thermostats, maintaining the set temperature in the room, do not take into account external factors such as solar radiation, inertia of the building or the presence of people, which leads to overheating or underheating of rooms. Systems with time programming, while allowing to set the schedule of the heating system, often do not correspond to the actual operating conditions, leading to energy overconsumption during non-working hours and discomfort during working hours [2].

Table 1

Comparison of traditional heating control systems.

Thus, the main limitations of traditional heating control methods include low accuracy and inertia, dependence on the human factor, lack of adaptation to changing conditions, high energy consumption and uneven heat distribution, as well as limited opportunities for optimising the system operation (Table 1).

In contrast to traditional approaches, intelligent heating control systems offer more efficient and adaptive methods based on the application of modern technologies. These systems utilise a variety of intelligent approaches including sensor networks, automatic control systems, adaptive and predictive control, machine learning algorithms, and building management systems (BMS) [3]. Sensor networks, which include multiple sensors distributed throughout a building, allow real-time monitoring of climate parameters such as temperature, humidity, and CO2 concentration. This allows for more precise control of the heating system, taking into account local conditions and the needs of each zone of the building. Automatic control systems use data from sensor networks to adjust the operation of the heaters, automatically maintaining the set temperature with minimal fluctuations. Adaptive control, in turn, adjusts heating system parameters in real time by analysing current conditions, including changes in weather, occupancy, and occupant preferences. More advanced adaptive systems can also take into account the inertia of the building, using thermal behaviour models to prevent overshoot.

Figure. 1. Schematic diagram of the building management system

Source: https://preora.com/akkumulyatornye-shkafy-w-bms/

Predictive control is based on predicting the heat load and optimising the operation of the heating system using historical data and weather forecasts. This allows not only to take into account the current temperature, but also to adjust the system operation in advance based on expected changes in weather conditions. Machine learning algorithms analyse large amounts of data collected by sensor networks and weather services to identify patterns and optimise heating system operation modes. These algorithms can learn from experience and improve their performance over time, adapting to specific building conditions. Building Management Systems (BMS) provide integration of all building engineering systems, including heating, ventilation and air conditioning, providing comprehensive energy management and process automation [4]. These systems use centralised controllers and software to control and monitor all connected systems (Fig. 1).

The key element of intelligent systems is the controller, which receives data from sensors, processes them using algorithms and sends control signals to actuators such as thermostatic valves, circulation pumps and fans. The principle scheme of operation of an intelligent heating control system is as follows: sensors measure the microclimate parameters and transmit the data to the controller. The controller, processing this data and using inbuilt algorithms, sends signals to the actuators to maintain the set temperature. At the same time, intelligent systems often use models of the building’s heat balance for more precise control. Examples of application of intelligent heating control systems can be found in smart homes, where integrated control systems automatically adjust the temperature depending on the presence of occupants, their preferences and weather conditions, in commercial buildings, where building management systems with zonal temperature control optimise energy consumption, and in industrial facilities, where predictive control and machine learning algorithms adapt the operation of the heating system to production processes and climatic conditions [5].

In conclusion, intelligent heating control systems represent an effective and promising method for improving the energy efficiency of buildings. Unlike traditional systems, intelligent systems provide more accurate, adaptive and automated control, which leads to a significant reduction in energy consumption, increased comfort and reduced negative environmental impact. Development prospects in this area are related to the development of more accurate heat load prediction models, improvements in machine learning and artificial intelligence algorithms, and the integration of various intelligent control systems into a single comprehensive platform. Further research and development should focus on reducing the cost of implementing intelligent systems and expanding their functionality. Intelligent heating control systems are an integral part of the future of energy efficient buildings and their widespread implementation plays a key role in achieving sustainable development goals.